MEPS HC-010D:
1996 Hospital Inpatient Stays
Agency for Healthcare Research and Quality
Center for Financing, Access, and Cost Trends
540 Gaither Road
Rockville, MD 20850
(301) 427-1406
Table of Contents
A. Data Use Agreement
B. Background
1.0 Household Component
2.0 Medical Provider Component
3.0 Insurance Component
4.0 Nursing Home Component
5.0 Survey Management
C. Technical and Programming Information
1.0 General Information
2.0 Data File Information
2.1 Codebook Structure
2.2 Reserved Codes
2.3 Codebook Format
2.4 Variable Naming
2.4.1 General
2.4.2 Expenditure and Sources of Payment Variables
2.5 File 1 Contents
2.5.1 Survey Administration Variables
2.5.1.1 Person Identifiers
(DUID, PID, DUPERSID
2.5.1.2Record Identifiers
(EVENTIDX, FFID11X)
2.5.2 Characteristics of Hospital Inpatient Stay Events
2.5.2.1 Start and End Dates of Event
(IPBEGDD-IPENDYR)
2.5.2.2 Length of Stay
(NUMNIGHX, NUMNIGHT)
2.5.2.3 Preceding ER Visits
(EMERROOM, ERHEVIDX)
2.5.2.4 Other Visit Detail
(SPECCOND - VAPLACE)
2.5.2.5 VA Facility
2.5.2.6 MPC Data Indicator
(MPCDATA)
2.5.2.7 Mother/Newborn Flag
(MBLINK)
2.5.3 Condition and Procedure Codes (IPICD1X-IPICD4X, IPPRO1X, IPPRO2X)
and Clinical Classification Codes (IPCCC1X-IPCCC4X)
2.5.3.1 Condition Record Count Variable
(NUMCOND
2.5.4 Flat Fee Variables
2.5.4.1 Definition of Flat Fee Payments
2.5.4.2 Flat Fee Variable Descriptions
2.5.4.3 Flat Fee Type
(FFIPTYPE)
2.5.4.4 Total Number of 1996 Events in Group (FFTOT96)
2.5.4.5 Caveats of Flat Fee Groups
2.5.6 Expenditure Data
2.5.6.1 Definition of Expenditures
2.5.6.2 Data Editing/Imputation Methodologies of Expenditure Variables
2.6 File 2 Contents: Pre-imputed Expenditure Variables
3.0 Sample Weights and Variance Estimation Variables (WTDPER96-VARPSU96)
3.1 Details on Person Weights Construction
4.0 Strategies for Estimation
4.1 Variables with Missing Values
4.2 Basic Estimates of Utilization, Expenditure and Sources of Payment
4.3 Estimates of the Number of Persons with Hospital Inpatient Stays
4.4 Person-Based Ratio Estimates
4.4.1 Person-Based Ratio Estimates Relative to Persons with Hospital Inpatient
Use
4.4.2 Person-Based Ratio Estimates Relative to the Entire Population
4.5 Sampling Weights for Merging Previous Releases of MEPS Household Data with the
Hospital Inpatient Stays Data File
4.6 Variance Estimation
5.0 Merging/Linking MEPS Data Files
5.1 Linking a Person-Level File to the Hospital Inpatient Stays File
5.2 Linking the Hospital Inpatient Stays (HC-010D) to the Medical Conditions File (HC-006) and/or the Prescribed Medicines File (HC-010A)
5.3 Limitations/Caveats of RXLK (the Prescribed Medicine Link File)
5.4 Limitations/Caveats of CLNK (the Medical Conditions Link File)
6.0 Programming Information
References
Attachment 1
D. Codebooks
(link to separate file)
E. Variable-Source Crosswalk
A. Data Use Agreement
Individual identifiers have been removed from the microdata contained in the files on this CD-ROM.
Nevertheless, under sections 308 (d) and 903 (c) of the Public Health Service Act (42 U.S.C. 242m
and 42 U.S.C. 299 a-1), data collected by the Agency for Healthcare Research and Quality (AHRQ)
and/or the National Center for Health Statistics (NCHS) may not be used for any purpose other than
for the purpose for which they were supplied; any effort to determine the identity of any reported
cases, is prohibited by law.
Therefore in accordance with the above referenced Federal statute, it is understood that:
- No one is to use the data in this data set in any way except for statistical reporting and
analysis.
If the identity of any person or establishment should be discovered inadvertently, then
(a) no use will be made of this knowledge, (b) the Director, Office of Management,
AHRQ will be advised of this incident, (c) the information that would identify any
individual or establishment will be safeguarded or destroyed, as requested by
AHRQ,
and (d) no one else will be informed of the discovered identity.
- No one will attempt to link this data set with individually identifiable records from any
data sets other than the Medical Expenditure Panel Survey or the National Health
Interview Survey.
By using these data you signify your agreement to comply with the above-stated statutorily based
requirements, with the knowledge that deliberately making a false statement in any matter within the
jurisdiction of any department or agency of the Federal Government violates 18 U.S.C. 1001 and is
punishable by a fine of up to $10,000 or up to 5 years in prison.
The Agency for Healthcare Research and Quality requests that users cite AHRQ and the Medical
Expenditure Panel Survey as the data source in any publications or research based upon these data.
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B. Background
This documentation describes one in a series of public use files from the Medical Expenditure Panel
Survey (MEPS). The survey provides a new and extensive data set on the use of health services and
health care in the United States.
MEPS is conducted to provide nationally representative estimates of health care use, expenditures,
sources of payment, and insurance coverage for the U.S. civilian noninstitutionalized population.
MEPS also includes a nationally representative survey of nursing homes and their residents. MEPS
is cosponsored by the Agency for Healthcare Research and Quality (AHRQ) (formerly the Agency
for Health Care Policy and Research (AHCPR)) and the National Center for Health Statistics
(NCHS).
MEPS comprises four component surveys: the Household Component (HC), the Medical Provider
Component (MPC), the Insurance Component (IC), and the Nursing Home Component (NHC). The
HC is the core survey, and it forms the basis for the MPC sample and part of the IC sample. The
separate NHC sample supplements the other MEPS components. Together these surveys yield
comprehensive data that provide national estimates of the level and distribution of health care use and
expenditures, support health services research, and can be used to assess health care policy
implications.
MEPS is the third in a series of national probability surveys conducted by AHRQ on the financing
and use of medical care in the United States. The National Medical Care Expenditure Survey
(NMCES, also known as NMES-1) was conducted in 1977. The National Medical Expenditure
Survey (NMES-2) was conducted in 1987. Beginning in 1996, MEPS continues this series with
design enhancements and efficiencies that provide a more current data resource to capture the
changing dynamics of the health care delivery and insurance system.
The design efficiencies incorporated into MEPS are in accordance with the Department of Health and
Human Services (DHHS) Survey Integration Plan of June 1995, which focused on consolidating
DHHS surveys, achieving cost efficiencies, reducing respondent burden, and enhancing analytical
capacities. To accommodate these goals, new MEPS design features include linkage with the
National Health Interview Survey (NHIS), from which the sampling frame for the MEPS HC is
drawn, and continuous longitudinal data collection for core survey components. The MEPS HC
augments NHIS by selecting a sample of NHIS respondents, collecting additional data on their health
care expenditures, and linking these data with additional information collected from the respondents'
medical providers, employers, and insurance providers.
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1.0 Household Component
The MEPS HC, a nationally representative survey of the U.S. civilian noninstitutionalized population,
collects medical expenditure data at both the person and household levels. The HC collects detailed
data on demographic characteristics, health conditions, health status, use of medical care services,
charges and payments, access to care, satisfaction with care, health insurance coverage, income, and
employment.
The HC uses an overlapping panel design in which data are collected through a preliminary contact
followed by a series of five rounds of interviews over a 2½-year period. Using computer-assisted
personal interviewing (CAPI) technology, data on medical expenditures and use for two calendar
years are collected from each household. This series of data collection rounds is launched each
subsequent year on a new sample of households to provide overlapping panels of survey data and,
when combined with other ongoing panels, will provide continuous and current estimates of health
care expenditures.
The sampling frame for the MEPS HC is drawn from respondents to NHIS, conducted by NCHS.
NHIS provides a nationally representative sample of the U.S. civilian noninstitutionalized population,
with oversampling of Hispanics and blacks.
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2.0 Medical Provider Component
The MEPS MPC supplements and validates information on medical care events reported in the MEPS
HC by contacting medical providers and pharmacies identified by household respondents. The MPC
sample includes all hospitals, hospital physicians, home health agencies, and pharmacies reported in
the HC. Also included in the MPC are all office-based physicians who:
- were identified by the household respondent as providing care for HC respondents
receiving Medicaid.
- were selected through a 75-percent sample of HC households receiving care through an
HMO (health maintenance organization) or managed care plan.
- were selected through a 25-percent sample of the remaining HC households.
Data are collected on medical and financial characteristics of medical and pharmacy events reported
by HC respondents, including:
- Diagnoses coded according to ICD-9-CM (9th Revision, International Classification of
Diseases) and DSM-IV (Fourth Edition, Diagnostic and Statistical Manual of Mental
Disorders).
- Physician procedure codes classified by CPT-4 (Common Procedure Terminology, Version
4).
- Inpatient stay codes classified by DRGs (diagnosis groups).
- Prescriptions coded by national drug code (NDC), medication name, strength, and quantity
dispensed.
- Charges, payments, and the reasons for any difference between charges and payments.
The MPC is conducted through telephone interviews and mailed survey materials. In some instances,
providers sent medical and billing records which were abstracted into the survey instruments.
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3.0 Insurance Component
The MEPS IC collects data on health insurance plans obtained through employers, unions, and other
sources of private health insurance. Data obtained in the IC include the number and types of private
insurance plans offered, benefits associated with these plans, premiums, contributions by employers
and employees, eligibility requirements, and employer characteristics.
Establishments participating in the MEPS IC are selected through four sampling frames:
- A list of employers or other insurance providers identified by MEPS HC respondents who
report having private health insurance at the Round 1 interview.
- A Bureau of the Census list frame of private-sector business establishments.
- The Census of Governments from Bureau of the Census.
- An Internal Revenue Service list of the self-employed.
To provide an integrated picture of health insurance, data collected from the first sampling frame
(employers and insurance providers) are linked back to data provided by the MEPS HC respondents.
Data from the other three sampling frames are collected to provide annual national and State estimates
of the supply of private health insurance available to American workers and to evaluate policy issues
pertaining to health insurance.
The MEPS IC is an annual survey. Data are collected from the selected organizations through a
prescreening telephone interview, a mailed questionnaire, and a telephone followup for
nonrespondents.
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4.0 Nursing Home Component
The 1996 MEPS NHC was a survey of nursing homes and persons residing in or admitted to nursing
homes at any time during calendar year 1996. The NHC gathered information on the demographic
characteristics, residence history, health and functional status, use of services, use of prescription
medicines, and health care expenditures of nursing home residents. Nursing home administrators and
designated staff also provided information on facility size, ownership, certification status, services
provided, revenues and expenses, and other facility characteristics. Data on the income, assets, family
relationships, and care-giving services for sampled nursing home residents were obtained from next-of-kin or other knowledgeable persons in the community.
The 1996 MEPS NHC sample was selected using a two-stage stratified probability design. In the first
stage, facilities were selected; in the second stage, facility residents were sampled, selecting both
persons in residence on January 1, 1996, and those admitted during the period January 1 through
December 31.
The sample frame for facilities was derived from the National Health Provider Inventory, which is
updated periodically by NCHS. The MEPS NHC data were collected in person in three rounds of
data collection over a 1½-year period using the CAPI system. Community data were collected by
telephone using computer-assisted telephone interviewing (CATI) technology. At the end of three
rounds of data collection, the sample consisted of 815 responding facilities, 3,209 residents in the
facility on January 1, and 2,690 eligible residents admitted during 1996.
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5.0 Survey Management
MEPS data are collected under the authority of the Public Health Service Act. They are edited and
published in accordance with the confidentiality provisions of this act and the Privacy Act. NCHS
provides consultation and technical assistance.
As soon as data collection and editing are completed, the MEPS survey data are released to the public
in staged releases of summary reports and microdata files. Summary reports are released as printed
documents and electronic files. Microdata files are released on CD-ROM and/or as electronic files.
Printed documents and CD-ROMs are available through the AHRQ Publications Clearinghouse.
Write or call:
AHRQ Publications Clearinghouse
Attn: (publication number)
P.O. Box 8547
Silver Spring, MD 20907
800/358-9295
410/381-3150 (callers outside the United States only)
888/586-6340 (toll-free TDD service; hearing impaired only)
Be sure to specify the AHRQ number of the document or CD-ROM you are requesting. Selected
electronic files are available from the Internet on the MEPS web site: <http://www.meps.ahrq.gov/>.
Additional information on MEPS is available from the MEPS project manager or the MEPS public
use data manager at the Center for Cost and Financing Studies, Agency for Healthcare Research and
Quality.
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C. Technical and Programming Information
1.0 General Information
This documentation describes one in a series of public use event files from the 1996 Medical
Expenditure Panel Survey Household (HC) and Medical Provider Components(MPC) . Released as
an ASCII data file and SAS transport file, this public use file provides detailed information on
hospital inpatient stays for a nationally representative sample of the civilian noninstitutionalized
population of the United States and can be used to make estimates of hospital inpatient stay utilization
and expenditures for calendar year 1996. Each record on this event file represents a unique hospital
inpatient stay event; that is, a hospital inpatient stay reported by the household respondent. In
addition to expenditures related to the stay, each record contains household reported medical
conditions and procedures associated with the hospitalization and information on the length of stay.
Data from this event file can be merged with other 1996 MEPS HC data files for purposes of
appending person characteristics such as demographic or health insurance coverage to each hospital
inpatient stay record.
Counts of hospital inpatient stay utilization are based entirely on household reports. Information from
the MEPS MPC was used to supplement expenditure and payment data reported by the household.
This file can be also used to construct summary variables of expenditures, sources of payment, and
related aspects of hospital inpatient care. Aggregate annual person-level information on the use of
hospital inpatient stays and other health services use is provided on public use file HC-011, where
each record represents a MEPS sampled person.
The following documentation offers a brief overview of the types and levels of data provided, the
content and structure of the files and the codebook, and programming information. It contains the
following sections:
Data File Information
Sample Weights and Variance Estimation Variables
Merging MEPS Data Files
Programming Information
References
Codebook
Variable to Source Crosswalk
For more information on MEPS HC survey design see S. Cohen, 1997; J. Cohen, 1997; and S. Cohen,
1996. For information on the MEPS MPC design, see S. Cohen, 1998. A copy of the survey
instruments used to collect the information on this file is available on the MEPS web site at the
following address: <http://www.meps.ahrq.gov>.
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2.0 Data File Information
This public use data set consists of 2 event-level data files. File 1 contains characteristics associated
with the hospital inpatient stay event and imputed expenditure data. File 2 contains pre-imputed and
unimputed expenditure data from both the Household and Medical Provider Components for all
hospital inpatient stay events on File 1. Please see the Attachment 1 for definitions of imputed, un-imputed and pre-imputed expenditure variables.
Both files 1 and 2 of this public use data set contains 2,207 hospital inpatient stay records. Of the
2,207 hospital inpatient stay records, 2,138 are associated with persons having a positive person-level
weight (WTDPER96). These files include hospital inpatient stay records for all household survey
respondents who resided in eligible responding households and reported at least one hospital inpatient
stay. Each record represents one household-reported hospital inpatient stay that occurred during
calendar year 1996. Hospital inpatient stays known to have occurred after December 31, 1996 are
not included on this file. Some household respondents may have multiple hospital inpatient stays and
thus will be represented in multiple records on this file. Other household respondents may have
reported no hospital inpatient stays and thus will have no records on this file. These data were
collected during rounds 1, 2, and 3 of the MEPS HC. The persons represented on this file had to meet
the following three criteria:
1) The hospital stay had to have been reported by a household survey respondent as an
inpatient hospital stay (regardless of a stay's length). Thus, the file contains some
hospitalizations that were reported as not including an overnight stay.
2) The hospital stay had to have ended during 1996. Stays that began prior to 1996, but ended
during 1996, are included on this file. Stays that began in 1996, but ended during 1997, are
excluded from this file and will be represented on a subsequent 1997 data file. Please note that
persons with no hospital inpatient stays use for 1996 are not included on this file (but are
represented on MEPS person-level files).
3) The persons represented on this file had to also meet either 3a or 3b:
a) Be classified as a key in-scope person who responded for his or her entire period
of 1996 eligibility (i.e., persons with a positive 1996 full-year person-level sampling
weight (WTDPER96 > 0)), or
b) Be classified as either an eligible non-key person or an eligible out-of-scope person
who responded for his or her entire period of 1996 eligibility, and belonged to a family
(i.e., all persons within a household (DUID) with the same value of FAMID) in which
all eligible family members responded for their entire period of 1996 eligibility, and
at least one family member has a positive 1996 full-year person weight (i.e., eligible
non-key or eligible out-of-scope persons who are members of a family all of whose
members have a positive 1996 full-year family-level weight (WTFAM96 >0)).
Please refer to Attachment 1 for definitions of key, non-key, inscope and eligible.
One caveat that should be noted is that in the case of a newborn, and the inpatient hospital stay
associated with the newborn's birth, a separate hospital inpatient stay record exists on the file only
if the newborn was discharged after the mother. Thus, hospital stays associated with a normal birth
are generally represented on the file as a single record (i.e., the mother's hospital inpatient stay record,
covering expenditure data for both the mother and baby). In situations where the newborn was
discharged after the mother, the birth event will be represented as two records (one record for the
mother and one record for the baby). For newborns re-admitted to the hospital during the reference
year, each subsequent re-admission will have a separate record.
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Each hospital inpatient stay record on File 1 includes the following: start and end dates of the hospital
inpatient stay; number of nights in the hospital; reason entered the hospital; main surgical procedure;
condition(s) associated with the hospital inpatient stay; medicines prescribed at discharge ; flat fee
information, imputed sources of payment, total payment and total charge for both the facility and
physician components of the hospital inpatient stay expenditure; and a full-year person-level weight.
File 2 of this public use data set is intended for analysts who want to perform their own imputations
to handle missing data. . This file contains one set of un-imputed expenditure information from the
Medical Provider Component as well as one set of pre-imputed expenditure information from the
Household Component. Both sets of expenditure data have been subject to minimal logical editing
that accounted for outliers, copayments or charges reported as total payments, and reimbursed
amounts that were reported as out of pocket payments. In addition, edits were implemented to correct
for misclassifications between Medicare and Medicaid and between Medicare HMO's and private
HMO's as payment sources. However, missing data was not imputed.
Data from these files can be merged with previously released 1996 MEPS HC person level data using
the unique person identifier, DUPERSID, to append person characteristics such as demographic or
health insurance characteristics to each record. Hospital inpatient stay events can also be linked to
the MEPS 1996 Medical Conditions File (HC-006) and the MEPS 1996 Prescribed Medicines File
(HC-10A). The Appendix File contains details on how to link MEPS data files.
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2.1 Codebook Structure
For each variable on these files, both weighted and unweighted frequencies are provided. The
codebook and data file sequence list variables in the following order:
File 1
Unique person identifiers
Unique hospital inpatient stay identifiers
Other survey administration variables
Hospital inpatient stay characteristics variables
ICD-9 codes
Clinical Classification Software codes
Imputed expenditure variables
Weight and variance estimation variables
File 2
Unique person identifiers
Unique hospital inpatient stay identifiers
Pre-imputed expenditure variables
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2.2 Reserved Codes
The following reserved code values are used:
VALUE DEFINITION
-1 INAPPLICABLE Question was not asked due to skip pattern.
-2 DETERMINED IN A PREVIOUS ROUND
-3 NO DATA IN ROUND
-5 NEVER WILL KNOW
-6 INAPPLICABLE Not asked due to person being under age 5
-7 REFUSED Question was asked and respondent refused to answer
question.
-8 DK Question was asked and respondent did not know answer.
-9 NOT ASCERTAINED Interviewer did not record the data.
-10 HOURLY WAGE VALUE SUPPRESSED Hourly Wage was suppressed.
-13 VALUE SUPPRESSED Value Suppressed
Generally, the values of -1,-7, -8, and -9 have not been edited on this file. The values of -1 and -9 can
be edited by analysts by following the skip patterns in the questionnaire.
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2.3 Codebook Format
This codebook describes an ASCII data set (although the data are also being provided in a SAS
transport file). The following codebook items are provided for each variable:
| IDENTIFIER |
DESCRIPTION |
| Name |
Variable name (maximum of 8
characters) |
| Description |
Variable descriptor (maximum 40
characters) |
| Format |
Number of bytes |
| Type |
Type of data: numeric (indicated by
NUM) or character (indicated by CHAR) |
| Start |
Beginning column position of
variable in record |
| End |
Ending column position of variable
in record |
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2.4 Variable Naming
In general, variable names reflect the content of the variable, with an 8 character limitation.
For questions asked in a specific round, the end digit in the variable name reflects the round in which
the question was asked. All imputed/edited variables end with an "X".
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2.4.1 General
Variables contained on Files 1, 2 and 3 were derived either from the HC questionnaire itself, the MPC
data collection instrument or from the CAPI. The source of each variable is identified in Appendix
1, entitled, "Variable to Source Crosswalk". Sources for each variable are indicated in one of three
ways: (1) variables which are derived from CAPI or assigned in sampling are so indicated; (2)
variables which come from one or more specific questions have those numbers and the questionnaire
section indicated in the "Source" column; (3) variables constructed from multiple questions using
complex algorithms are labeled "Constructed" in the "Source" column and (4) variables which have
been imputed are so indicated..
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2.4.2 Expenditure and Sources of Payment Variables
Both pre-imputed and imputed versions of the expenditure and sources of payment variables are
provided on 2 separate files. Expenditure variables on Files 1 and 2 of the MEPS event files follow
a standard naming convention and are 8 characters in length. Please note that pre-imputed means that
a series of logical edits have been performed on the variable but missing data remains. The imputed
versions incorporate the same edits but have also undergone an imputation process to account for
missing data.
The pre-imputed expenditure variables on File 2 end with an "H", if the data source was from the
MEPS Household Component and ends with a "M" if the data source was the MEPS Medical
Provider Component. All imputed variables on File 1 end with an "X".
The total sum of payments, 12 sources of payment variables and total charge variables are named
consistently in the following way:
The first two characters indicate the type of event:
IP - inpatient stay
OB - office-based visit
ER - emergency room visit
OP - outpatient visit
HH - home health visit
DV - dental visit
OM - other medical equipment
RX - prescribed medicine
For expenditure variables on these files, the third character indicates whether the expenditure is
associated with the facility (F) or the physician (P).
In the case of the source of payment variables, the fourth and fifth characters indicate:
SF - self or family
OF - other Federal Government
XP - sum of payments
MR - Medicare
SL - State/local government
MD - Medicaid
WC - Worker's Compensation
PV - private insurance
OT - other insurance
VA - Veterans
OR - other private
CH - CHAMPUS/CHAMPVA
OU - other public
The sixth and seventh characters indicate the year (96). The eighth character of all imputed/edited
variables is an " X".
For example, IPFSF96X is the edited/imputed amount paid by self or family for the facility portion
of the hospital inpatient stay expenditure incurred in 1996.
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2.5 File 1 Contents
2.5.1 Survey Administration Variables
2.5.1.1 Person Identifiers (DUID, PID, DUPERSID)
The dwelling unit ID (DUID) is a 5-digit random number assigned after the case was sampled for
MEPS. The 3-digit person number (PID) uniquely identifies each person within the dwelling unit. The
8-character variable DUPERSID uniquely identifies each person represented on the file and is the
combination of the variables DUID and PID. For detailed information on dwelling units and families,
please refer to the documentation on public use file HC-008.
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2.5.1.2 Record Identifiers (EVENTIDX, FFID11X)
EVENTIDX uniquely identifies each event/stay (i.e. each record on the file) and is the variable
required to link hospital inpatient stay events to data files containing details on conditions and/or
prescribed medicines (HC-006 and H-010A, respectively). For details on linking see Section 5.0.
FFID11X uniquely identifies a flat fee group, that is, all events that were part of a flat fee payment
situation. For example, dialysis treatments are typically covered in a flat fee arrangement where all
visits are covered under one flat fee dollar amount. These events have the same value for FFID11X.
Please note that FFID11X should be used to link up all MEPS event files (excluding prescribed
medicines) in order to determine the full set of events that are part of a flat fee group.
EVENTRN indicates the round in which the hospital inpatient stay was first reported.
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2.5.2 Characteristics of Hospital Inpatient Stay Events
File 1 contains 86 variables describing hospital inpatient stays reported by respondents in the Hospital
Stays section of the MEPS HC questionnaire. The questionnaire contains probes for determining
specific details about the hospital inpatient stay. Unless noted otherwise, the following variables are
provided as unedited.
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2.5.2.1 Start and End Dates of Event (IPBEGDD-IPENDYR)
File 1 contains variables describing hospital inpatient stays reported by household respondents in the
Hospital Section of the MEPS HC questionnaire. There are three variables which indicate the day,
month and year a hospital stay began (IPBEGDD, IPBEGMM, IPBEGYR, respectively). Similarly,
there are three variables which indicate the day, month and year a hospital stay ended (IPENDDD,
IPENDMM, IPENDYR, respectively). These variables have not been edited.
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2.5.2.2 Length of Stay (NUMNIGHX, NUMNIGHT)
The edited variable NUMNIGHX denotes length of hospital stay. For stays beginning in 1995 and
ending in 1996, this variable would include the nights associated with 1995. It was edited using the
above mentioned begin and end dates of the hospital inpatient stay. If the dates were unknown, then
the unedited variable NUMNIGHT(number of nights in the hospital) was used. Users should note that
NUMNIGHT was only asked for events with missing date information. Hence, it contains large
amounts of missing data and cannot be used alone but rather in conjunction with date information.
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2.5.2.3 Preceding ER Visits (EMERROOM, ERHEVIDX)
The variable EMERROOM was derived directly from the Hospital Stays section of the HC survey
instrument and is provided as unedited. EMERROOM describes whether the hospital inpatient stay
began with an emergency room visit. Users should be aware that no attempt was made to reconcile
EMERROOM with information from the Emergency Room Visit File. Discrepancies do exist where
the hospital stays record indicates that there is a preceding emergency room visit but no such visit
exists on the Emergency Room File.
The variable ERHEVIDX is a constructed variable which identifies hospital stays whose expenditures
include the expenditures for the preceding emergency room visit. This variable was constructed by
comparing date information for the reported hospital stay and all emergency room visits for the same
person. ERHEVIDX has not been reconciled with the unedited variable EMERROOM.
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2.5.2.4 Other Visit Detail (SPECCOND - VAPLACE)
Also provided are the following unedited variables: hospital inpatient stay related to condition
(SPECCOND), reason entered hospital (RSNINHOS), any operation or surgery performed while
respondent was in hospital (ANYOPER) and if surgery performed then what was the main surgical
procedure (SURGPROC), any medicine prescribed at discharge (DSCHPMED), and finally, any
physician seen outside the hospital (DROUTSID).
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2.5.2.5 VA Facility
VAPLACE is a constructed variable that indicates whether the provider worked at a VA facility. This
only has valid data for providers that were sampled into the Medical Provider Component. All other
providers are classified as unknown.
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2.5.2.6 MPC Data Indicator (MPCDATA)
While all hospital inpatient events are sampled into the Medical Provider Component, not all hospital
inpatient stay records have MPC data associated with them. This is dependent upon the cooperation
of the household respondent to provide permission forms to contact the hospital as well as the
cooperation of the hospital to participate in the survey. MPCDATA indicates whether or not MPC
data was collected for the hospital inpatient stay.
2.5.2.7 Mother/Newborn Flag (MBLINK)
The variable MBLINK flags hospital stays where expenditures for the delivery of a newborn are
included in the mother's record. See Section 2.5.6.2 for details on mother/newborn expenditures .
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2.5.3 Condition and Procedure Codes (IPICD1X-IPICD4X, IPPRO1X, IPPRO2X) and
Clinical Classification Codes (IPCCC1X-IPCCC4X)
Information on household reported medical
conditions and procedures associated with each hospital inpatient
stay event are provided on this file. There are up to four condition
codes (IPICD1X-IPICD4X) and up to two procedure codes (IPPRO1X
and IPPRO2X) listed for each hospital inpatient
stay event (99.5% of hospital inpatient stay events have 0-3 condition
records linked). In order to obtain complete condition information
associated with an event, the analyst must link to the HC-006
Medical Conditions File. Details on how to link this file to the
MEPS Medical Conditions File (HC-006) are provided in Section 5.0.
The user should note that due to confidentiality restrictions,
provider reported condition information are not publicly available.
The medical conditions and procedures reported by the Household Component respondent were
recorded by the interviewer as verbatim text, which were then coded to fully-specified 1996 ICD-9-CM codes, including medical condition and V codes (see Health Care Financing Administration,
1980), by professional coders. Although codes were verified and error rates did not exceed 2.5 percent
for any coder, analysts should not presume this level of precision in the data; the ability of household
respondents to report condition data that can be coded accurately should not be assumed (see Cox and
Cohen, 1985; Cox and Iachan, 1987; Edwards, et al, 1994; and Johnson and Sanchez, 1993). For
detailed information on conditions, please refer to the documentation on HC-006 1996 Medical
Condition File. For frequencies of conditions by event type, please see the Appendix File.
The ICD-9-CM condition and procedure codes were aggregated into clinically meaningful categories.
These categories, included on the file as IPCCC1X-IPCCC4X, were generated using Clinical
Classification Software (formerly known as Clinical Classifications for Health Care Policy Research
(CCHPR)), (Elixhauser, et al., 1998), which aggregates conditions and V-codes into 260 mutually
exclusive categories, most of which are clinically homogeneous.
In order to preserve respondent confidentiality, nearly all of the condition codes provided on this file
have been collapsed from fully-specified codes to 3-digit code categories. The reported ICD-9-CM
code values were mapped to the appropriate clinical classification category prior to being collapsed
to the 3-digit categories.
The condition and procedure codes (and clinical classification codes) linked to each hospital inpatient
stay event are sequenced in the order in which the conditions were reported by the household
respondent, which was in chronological order of occurrence and not in order of importance or
severity. Analysts who use the HC-006 Medical Conditions file in conjunction with this hospital
inpatient stay event file should note that the order of conditions on this file is not identical to that on
the Medical Conditions file.
The user should note that due to the design of the HC survey instrument, most hospital stays that are
reported as being for a delivery (RSNINHOS=4) link to condition codes that are for pregnancy rather
than a delivery. In addition, RSNINHOS has not been reconciled with the ICD-9 condition codes,
procedure codes nor CCC codes that are on the file.
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2.5.3.1 Condition Record Count Variable (NUMCOND)
The variable NUMCOND indicates the total number of condition records which can be linked from
HC-006: Medical Conditions File to each hospital inpatient stay event. For events where no condition
records linked (NUMCOND=0), the condition and procedure and clinical classification code variables
all have a value of -1 INAPPLICABLE. Similarly, for events without a linked second or third
condition record, the corresponding second or third condition and procedure and clinical classification
code variable was set to -1 INAPPLICABLE.
In order to obtain complete condition information for events with NUMCOND greater than 3, the
analyst must link to the MEPS Condition File (HC-006). Please see Section 5.0 for details on linking
MEPS data files.
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2.5.4 Flat Fee Variables
2.5.4.1 Definition of Flat Fee Payments
A flat fee is the fixed dollar amount a person is charged for a package of health care services.
Examples would be: obstetrician's fee covering a normal delivery, as well as pre- and post-natal care;
or a surgeon's fee covering surgical procedure and post-surgical care. A flat fee group is the set of
medical services (i.e., events) that are covered under the same flat fee payment situation. The flat fee
groups represented on this file (and all of the other 1996 MEPS event files), include flat fee groups
where at least one of the health care events, as reported by the HC respondent, occurred during 1996.
By definition a flat fee group can span multiple years and/or event types (e.g., hospital stay, physician
office visit), and a single person can have multiple flat fee groups.
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2.5.4.2 Flat Fee Variable Descriptions
There are several variables on this file that describe a flat fee payment situation and the number of
medical events that are part of a flat fee group. As noted previously, for a person, the variable
FFID11X can be used to identify all events, that are part of the same flat fee group. To identify such
events, FFID11X should be used to link events from all MEPS event files (excluding prescribed
medicines HC-010A). For the hospital stays that are not part of a flat fee payment situation, the flat
fee variables described below are all set to inapplicable (-1).
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2.5.4.3 Flat Fee Type (FFIPTYPE)
FFIPTYPE indicates whether the 1996 hospital stay is the "stem" or "leaf" of a flat fee group. A stem
(records with FFIPTYPE = 1) is the initial medical service (event) which is followed by other medical
events that are covered under the same flat fee payment. The leaf of the flat fee group (records with
FFIPTYPE = 2) are those medical events that are tied back to the initial medical event (the stem) in
the flat fee group. These "leaf" records have their expenditure variables set to zero.
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2.5.4.4 Total Number of 1996 Events in Group (FFTOT96)
If a hospital stay is part of a flat fee group, the variable FFTOT96 counts the total number of all
known events (that occurred during 1996) covered under a single flat fee payment situation. This
count includes the hospital stay record in the count.
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2.5.4.5 Caveats of Flat Fee Groups
The user should note that flat fee payment situations are not common with respect to hospital
inpatient stays . Hence, there are only 18 hospital inpatient stay events that are identified as being
part of a flat fee payment group. In order to correctly identify all events that are part of a flat fee
group, the user should link all MEPS event files (excluding the prescribed medicine file: HC-010A) using the variable FFID11X.
In general, every flat fee group should have an initial visit (stem) and at least one subsequent visit
(leaf). There are some situations where this is not true. For some of these flat fee groups, the
initial visit reported occurred in 1996 but the remaining visits that were part of this flat fee group
occurred in 1997. In this case, the 1996 flat fee group would consist of one event (the stem). The
1997 events that are part of this flat fee group are not represented on the file. Similarly, the
household respondent may have reported a flat fee group where the initial visit began in 1995 but
subsequent visits occurred during 1996. In this case, the initial visit would not be represented on
the file. This 1996 flat fee group would then only consist of one or more leaf records and no stem.
Another reason for which a flat fee group would not have a stem and a leaf record is that the stems
or leaves could have been reported as different event types. In a small number of cases, there are
flat fee bundles that span various event types. The stem may have been reported as one event type
and the leaves may have been reported as another event type. In order to determine this, the
analyst must link all event files (excluding the prescribed medicine file: HC-010A) using the
variable FFID11X to create the flat fee group.
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2.5.6 Expenditure Data
2.5.6.1 Definition of Expenditures
Expenditures on this file refer to what is paid for health care services. More specifically,
expenditures in MEPS are defined as the sum of payments for care received for each hospital stay,
including out of pocket payments and payments made by private insurance, Medicaid, Medicare
and other sources. The definition of expenditures used in MEPS differs slightly from its
predecessors: the 1987 NMES and 1977 NMCES surveys where "charges" rather than sum of
payments were used to measure expenditures. This change was adopted because charges became
a less appropriate proxy for medical expenditures during the 1990's due to the increasingly
common practice of discounting. Although measuring expenditures as the sum of payments
incorporates discounts in the MEPS expenditure estimates, these estimates do not incorporate any
payment not directly tied to specific medical care visits, such as bonuses or retrospective payment
adjustments paid by third party payers. Another general change from the two prior surveys is that
charges associated with uncollected liability, bad debt, and charitable care (unless provided by a
public clinic or hospital) are not counted as expenditures because there are no payments
associated with those classifications. While charge data is provided on this file, analysts should
use caution when working with this data because a charge does not typically represent actual
dollars exchanged for services or the resource costs of those services, nor are they directly
comparable to the expenditures defined in the 1987 NMES. For details on expenditure definitions,
please reference the following, "Informing American Health Care Policy" (Monheit, Wilson,
Arnett, 1999).
Expenditure data related to hospital events are broken out by facility and separately billing doctor
expenditures. This file contains five categories of expenditure variables per stay: basic hospital
facility expenses; expenses for doctors who billed separately from the hospital for any inpatient
services provided during hospital stay; total expenses, which is the sum of the facility and
physician expenses; facility total charge and physician total charge.
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2.5.6.2 Data Editing/Imputation Methodologies of Expenditure Variables
General Imputation Methodology
The expenditure data included on this file were derived from both the MEPS Household (HC) and
Medical Provider Components (MPC). The MPC contacted medical providers identified by
household respondents. The charge and payment data from medical providers was used in the
expenditure imputation process to supplement missing household data. For all hospital inpatient
stays, MPC data were used if complete; otherwise HC data were used if complete. Missing data
for hospital inpatient stays where HC data were not complete and MPC data were not collected or
complete were derived through the imputation process.
Logical edits were used to resolve internal inconsistencies and other problems in the HC and MPC
survey-reported data. The edits were designed to preserve partial payment data from households
and providers, and to identify actual and potential sources of payment for each household-reported
event. In general, these edits accounted for outliers, copayments or charges reported as total
payments, and reimbursed amounts that were reported as out of pocket payments. In addition,
edits were implemented to correct for misclassifications between Medicare and Medicaid and
between Medicare HMO's and private HMO's as payment sources. These edits produced a
complete vector of expenditures for some events, and provided the starting point for imputing
missing expenditures in the remaining events.
A weighted sequential hot-deck procedure was used to impute for missing expenditures as well as
total charge. The procedure uses survey data from respondents to replace missing data, while
taking into account the respondents' weighted distribution in the imputation process.
Classification variables vary by event type in the hot-deck imputations, but total charge and
insurance coverage are key variables in all of the imputations. Separate imputations were
performed for nine categories of medical provider care: inpatient hospital stays, outpatient
hospital department visits, emergency room visits, visits to physicians, visits to non-physician
providers, dental services, home health care by certified providers, home health care by paid
independents, and other medical expenses. After the imputations were finished, visits to
physician and non-physician providers were combined into a single medical provider file. The
two categories of home care also were combined into a single home health file.
Expenditures for services provided by separately billing doctors in hospital settings were also
edited and imputed. These expenditures are shown separately from hospital facility charges for
hospital inpatient, outpatient, and emergency room care.
Capitation Imputation
The imputation process was also used to make expenditure estimates at the event level for events
that were paid on a capitated basis. The capitation imputation procedure was designed as
reasonable approach to complete event level expenditures for respondents in managed care plans.
This procedure was conducted in two stages. First, HMO events reported in the MPC as covered
by capitation arrangements were imputed using similar HMO events paid on a fee-for-service,
with total charge as a key variable. Then this completed set of MPC events was used as the donor
pool for unmatched household-reported events for sample persons in HMOs. By using this
strategy, capitated HMO events were imputed as if the provider were reimbursed from the HMO
on a discounted fee-for-service basis.
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Imputation Methodology for Hospital Inpatient Stays
Facility expenditures for inpatient hospital stays were developed in a sequence of logical edits and
imputations. "Household" edits were applied to sources and amounts of payment for all events
reported by HC respondents. "MPC" edits were applied to provider-reported sources and amounts
of payment for records matched to household-reported events. Both sets of edits were used to
correct obvious errors (as described above) in the reporting of expenditures. After the data from
each source were edited, a decision was made as to whether household- or MPC-reported
information would be used in the final editing and hot-deck imputations for missing expenditures.
The general rule was that MPC data would be used for events where a household reported event
corresponded to a MPC reported event (i.e. a matched event), since providers usually have more
complete and accurate data on sources and amounts of payment than households.
Separate imputations were performed for flat fee and simple events. Most inpatient hospital stays
were imputed as simple events because facility charges for an inpatient hospital stay are rarely
grouped with other events. (See Section 2.5.4 for details on flat fee groups.)
Logical edits also were used to sort each event into a specific category for the imputations. Events
with complete expenditures were flagged as potential donors for the hot-deck imputations, while
events with missing expenditure data were assigned to various recipient categories. Each event
was assigned to a recipient category based on its pattern of missing data. For example, an event
with a known total charge but no expenditures information were assigned to one category, while
an event with a known total charge and some expenditures information was assigned to a different
category. Similarly, events without a known total charge were assigned to various recipient
categories based on the amount of missing data.
The logical edits produced eight recipient categories in which all events had a common pattern of
missing data. Separate hot-deck imputations were performed on events in each recipient category,
and the donor pool was restricted to events with complete expenditures from the MPC. The donor
pool restriction was used even though some unmatched events had complete household-reported
expenditures. These events were not allowed to donate information to other events because the
MPC data were considered to be more reliable.
The donor pool included "free events" because, in some instances, providers are not paid for their
services. These events represent charity care, bad debt, provider failure to bill, and third party
payer restrictions on reimbursement in certain circumstances. If free events were excluded from
the donor pool, total expenditures would be over-counted because the cost of free care would be
both implicitly included in paid events, and explicitly included in events that should have been
treated as free from provider.
Flat Fee Expenditures
The approach used to count expenditures for flat fees was to place the expenditure on the first
visit of the flat fee group. The remaining visits have zero payments. Thus, if the first visit in the
flat fee group occurred prior to 1996, all of the events that occurred in 1996 will have zero
payments. Conversely, if the first event in the flat fee group occurred at the end of 1996, the total
expenditure for the entire flat fee group will be on that event, regardless of the number of events it
covered after 1996. See section 2.5.4 for details on the flat fee variables.
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Zero Expenditures
There are some medical events reported by respondents where the payments were zero. This
could occur for several reasons including (1) free care was provided, (2) bad debt was incurred,
(3) care was covered under a flat fee arrangement beginning in an earlier year, or (4) follow-up
visits were provided without a separate charge (e.g., after a surgical procedure). If all of the
medical events for a person fell into one of these categories, then the total annual expenditures for
that person would be zero.
Discount Adjustment Factor
An adjustment was also applied to some HC reported expenditure data because an evaluation of
matched HC/MPC data showed that respondents who reported that charges and payments were
equal were often unaware that insurance payments for the care had been based on a discounted
charge. To compensate for this systematic reporting error, a weighted sequential hot-deck
imputation procedure was implemented to determine an adjustment factor for HC reported
insurance payments when charges and payments were reported to be equal. As for the other
imputations, selected predictor variables were used to form groups of donor and recipient events
for the imputation process.
Mother/Newborn Expenditures
Expenditure data for newborns were edited to exclude discharges after birth when the newborn
left the hospital on the same day as the mother. As a result, inpatient expenditures reported for
1996 births were usually applied to the mother and not treated as separate expenditures for the
infant. However, if a newborn was discharged at a later date than the mother, then the
hospitalization was treated as a separate hospital stay for the newborn.
This means that in most cases, expenditure data for the newborn is included on the mother's
record. A separate record for the newborn only exists if the newborn was discharged after the
mother. In this case, the expenditure for the newborn is on the newborn's record.
In addition, the user should note that for the purposes of the expenditure imputation, deliveries
were identified using the variable RSINHOS which has not been reconciled with pregnancy and
delivery ICD-9 codes on this file as well as on HC-006. As mentioned previously, in most
instances where RSINHOS= 4 delivery, the ICD-9 code indicates a pregnancy rather than a
delivery.
Hospital/Emergency Room Expenditures
Although a person may have indicated that there was an emergency room visit that preceded this
hospital stay (EMEROOM), there was no verification that, in fact, the emergency room visit was
actually recorded within the Emergency Room Section of the questionnaire.
While it is true that all of the event files can be linked by DUPERSID, there is no unique record
link between inpatient stays and emergency room visits. That is, a person could have one inpatient
stay and three emergency room visits during the calendar year. While the inpatient stay record
may indicate that it was preceded by an emergency room visit, there is no unique record link to the
appropriate (of the three) emergency room visit. However, where this relationship could be
identified (using MPC start and end date of the events as well as information from the provider),
the expenditure associated with the emergency room visit was moved to the hospital facility
expenditure (see Section 2.5.2.3). Hence, for some hospital stays, expenditures for a preceding
emergency room visits are included. In these situations, the corresponding emergency room record
on HC-010E: Emergency Room Visit File will have its expenditure information zeroed out to
avoid double-counting. The variable ERHEVIDX identifies these hospital stays whose
expenditures include the expenditures for the preceding emergency room visit. It should also be
noted that for these cases, there is only one hospital stay associated with the emergency room stay.
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Sources of Payment
In addition to total expenditures, variables are provided which itemize expenditures according to
major sources of payment categories. These categories are:
1. Out of pocket by user or family
2. Medicare
3. Medicaid
4. Private Insurance
5. Veteran's Administration, excluding CHAMPVA
6. CHAMPUS or CHAMPVA
7. Other Federal sources - includes Indian Health Service, Military Treatment Facilities, and other
care by the Federal government
8. Other State and Local Source - includes community and neighborhood clinics, State and local
health departments, and State programs other than Medicaid.
9. Worker's Compensation
10. Other Unclassified Sources - includes sources such as automobile, homeowner's, liability, and
other miscellaneous or unknown sources.
Two additional source of payment variables were created to classify payments for events with
apparent inconsistencies between insurance coverage and sources of payment based on data
collected in the survey. These variables include:
11. Other Private - any type of private
insurance payments reported for persons not reported to have any
private health insurance coverage during the year as defined in
MEPS; and
12. Other Public - Medicaid payments reported for persons who were not reported to be enrolled
in the Medicaid program at any time during the year.
Though relatively small in magnitude, users should exercise caution when interpreting the
expenditures associated with these two additional sources of payment. While these payments
stem from apparent inconsistent responses to health insurance and source of payment questions in
the survey, some of these inconsistencies may have logical explanations. For example, private
insurance coverage in MEPS is defined as having a major medical plan covering hospital and
physician services. If a MEPS sampled person did not have such coverage but had a single
service type insurance plan (e.g. dental insurance) that paid for a particular episode of care, those
payments may be classified as "other private". Some of the "other public" payments may stem
from confusion between Medicaid and other state and local programs or may be from persons who
were not enrolled in Medicaid, but were presumed eligible by a provider who ultimately received
payments from the program.
Users should also note that the Other Public and Other private source of payment categories only
exist on File 1 for imputed expenditure data since they were created through the
editing/imputation process. File 2 reflects 10 sources of payment as they were collected through
the survey instrument.
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Imputed Hospital Inpatient Stay Expenditure Variables
This file contains 2 sets of imputed expenditure variables: facility expenditures and physician
expenditures.
Hospital Inpatient Facility Expenditures (IPFSF96X-IPFOT96X, IPFTC96X, IPFXP96X)
Hospital facility expenses include all expenses for direct hospital care, including room and board,
diagnostic and laboratory work, x-rays, and similar charges, as well as any physician services
included in the hospital charge.
Hospital facility expenditures were obtained primarily through the MPC. If the physician charges
were included in the hospital bill, then this expenditure is included in the facility expenditure
variables. The imputed facility expenditures are provided on this file. IPFSF96X - IPFOT96X are
the 12 sources of payment, IPFTC96X is the total charge, and IPFXP96X is the sum of the 12
sources of payments for the facility expenditure. The 12 source of payment categories are:
self/family, Medicare, Medicaid, private insurance, Veterans Administration,
CHAMPUS/CHAMPVA, other federal, state/local governments, Workman's Compensation,
other private insurance, other public insurance and other insurance.
Hospital Inpatient Physician Expenditures (IPDSF96X - IPDOT96X, IPDTC96X,
IPDXP96X)
Separately billing doctor (SBD) expenses typically cover services provided to patients in hospital
settings by providers like anesthesiologists, radiologists, and pathologists, whose charges are often
not included in hospital bills.
For medical doctors who bill separately (i.e. outside the hospital bill), a separate data collection
effort within the Medical Provider Component was performed to obtain this same set of
expenditure information from each separately billing doctor. It should be noted that there could
be several separately billing doctors associated with a medical event. For example, a hospital
inpatient stay could have a radiologist, anesthesiologist, pathologist and a surgeon associated with
it. If their services are not included in the hospital bill then this is one medical event with 4
separately billing doctors. The imputed expenditure information associated with the separately
billing doctors for a hospital inpatient stay (i.e. the expenditures incurred by the radiologist +
anesthesiologist + pathologist + surgeon) and is provided on the file. IPDSF96X - IPDOT96X
are the 12 sources of payment, IPDTC96X is the total charge, and IPDXP96X is the sum of the 12
sources of payments.
Analysts need to take into consideration whether to analyze facility and SBD expenditures
separately, combine them within service categories, or collapse them across service categories
(e.g. combine SBD expenditures with expenditures for physician visits to offices and/or outpatient
departments). Analysts interested in total expenditure should use the variable IPEXP96X, which
includes both the facility and physician amounts.
Rounding
Expenditure variables on File 1 have been rounded to the nearest penny. Person level expenditure
information released on HC-011 were rounded to the nearest dollar. It should be noted that using
the MEPS event files HC-010A through HC-010H to create person level totals will yield slightly
different totals than that found on HC-011. These differences are due to rounding only.
Moreover, in some instances, the number of persons having expenditures on the event files (HC-010A-HC-010H) for a particular source of payment may differ from the number of persons with
expenditures on the person level expenditure file (HC-011) for that source of payment This
difference is also an artifact of rounding only. Please see the Appendix File for details on such
rounding differences.
Imputation Flags (IMPIPFSF - IMPIPCHG)
The variables IMPIPFSF - IMPIPCHG identify records where sources of payment and total charge
for the facility portion of the expenditure have been imputed using the methodologies outlined in
this document. The variable IMPIPNUM indicates the number of physician records associated
with the hospital stay where the physician portion of the expenditures have been imputed. It is not
available for individual sources of payment.
When a record was identified as being the leaf of a flat fee group, the values of all imputation
flags were set to "0" (not imputed) since they were not included in the imputation process.
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2.6 File 2 Contents: Pre-imputed Expenditure Variables
Both imputed and pre-imputed expenditure data are provided on this file. Pre-imputed means that
only a series of logical edits were applied to both the HC and MPC data to correct for several
problems including outliers, copayments or charges reported as total payments, and reimbursed
amounts counted as out of pocket payments. Edits were also implemented to correct for
misclassifications between Medicare and Medicaid and between Medicare HMO's and private
HMO's as payment sources as well as a number of other data inconsistencies that could be
resolved through logical edits. Missing data were not imputed.
As described previously, there are essentially two components that went into creating the total
medical expenditure variable: household reported expenditure data and provider reported
expenditure data. Both sets of expenditure data are provided in their pre-imputed form and have
not gone through the same level of quality control as their imputed counterpart. This means that
(in some instances) there are large amounts of missing data. The household and provider reported
facility pre-imputed expenditure data are provided on this file (IPSF96H - IPOT96H and
IPFSF96M-IPFOT96M respectively).
The user shall note that there exist only 10 sources of payment variables in the pre-imputed
expenditure data, while the imputed expenditure data on File 1 contains 12 sources of payment
variables. The additional two sources of payment (which are not reported as separate sources of
payment through the data collection) are Other Private and Other Public. These sources of
payment categories were constructed to resolve apparent inconsistencies between individuals'
reported insurance coverage and their sources of payment for specific events..
The user should also note that the variable HHSFFIDX is the original flat fee identifier that was
derived during the household interview. This identifier should only be used if the analyst is
interested in performing their own expenditure imputation.
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3.0 Sample Weights and Variance Estimation Variables (WTDPER96-VARPSU96)
Overview
There is a single full year person-level weight (WTDPER96) included on this file. A person-level
weight was assigned to each hospital inpatient stay reported by a key, in-scope person who
responded to MEPS for the full period of time that he or she was in scope during 1996. A key
person either was a member of an NHIS household at the time of the NHIS interview, or became a
member of such a household after being out-of-scope at the time of the 1995 NHIS (examples of
the latter situation include newborns and persons returning from military service, an institution, or
living outside the United States). A person is in scope whenever he or she is a member of the
civilian noninstitutionalized portion of the U.S. population.
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3.1 Details on Person Weights Construction
The person-level weight WTDPER96 was developed using the MEPS Round 1 person-level
weight as a base weight (for key, in scope respondents who joined an RU after Round 1, the
Round 1 RU weight served as a base weight). The weighting process included an adjustment for
nonresponse over Round 2 and the 1996 portion of Round 3, as well as poststratification to
population control figures for December 1996 (these figures were derived by scaling the
population totals obtained from the March 1997 Current Population Survey (CPS) to reflect the
Census Bureau estimated population distribution across age and sex categories as of December,
1996). Variables used in the establishment of person-level poststratification control figures
included: poverty status (below poverty, from 100 to 125 percent of poverty, from 125 to 200
percent of poverty, from 200 to 400 percent of poverty, at least 400 percent of poverty); census
region (Northeast, Midwest, South, West); MSA status (MSA, non-MSA); race/ethnicity
(Hispanic, black but non-Hispanic, and other); sex; and age. Overall, the weighted population
estimate for the civilian non-institutionalized population for December 31, 1996 is 265,439,511
persons. The inclusion of key, in scope persons who were not in scope on December 31,1996
brings the estimated total number of persons represented by the MEPS respondents over the
course of the year up to 268,905,490 (WTDPER96 > 0). The weighting process included
poststratification to population totals obtained from the 1996 Medicare Current Beneficiary
Survey (MCBS) for the number of deaths among Medicare beneficiaries in 1996, and
poststratification to population totals obtained from the 1996 MEPS Nursing Home Component
for the number of individuals admitted to nursing homes.
The MEPS Round 1 weights incorporated the following components: the original household
probability of selection for the NHIS; ratio-adjustment to NHIS national population estimates at
the household (occupied dwelling unit) level; adjustment for nonresponse at the dwelling unit
level for Round 1; and poststratification to figures at the family- and person-level obtained from
the March 1996 CPS database.
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4.0 Strategies for Estimation
This file is constructed for efficient estimation of utilization, expenditure, and sources of payment
for hospital inpatient care and to allow for estimates of number of persons with inpatient hospital
utilization for 1996 (defined as discharges in 1996).
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4.1 Variables with Missing Values
It is essential that the analyst examine all variables for the presence of negative values used to
represent missing values. For example, a record with a value of -8 for the first ICD9 condition
code (IPICD1X) indicates that the condition was reported as unknown.
For continuous or discrete variables, where means or totals may be taken, it may be necessary to
set minus values to values appropriate to the analytic needs. That is, the analyst should either
impute a value or set the value to one that will be interpreted as missing by the computing
language used. For categorical and dichotomous variables, the analyst may want to consider
whether to recode or impute a value for cases with negative values or whether to exclude or
include such cases in the numerator and/or denominator when calculating proportions.
Methodologies used for the editing/imputation of expenditure variables (e.g. sources of payment
flat fee, mom/baby, hospital/er, and zero expenditures) are described in Section 2.5.6.2.
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4.2 Basic Estimates of Utilization, Expenditure and Sources of Payment
W
hile the examples described below illustrate the use of event level data in constructing person
level total expenditures, these estimates can also be derived from the person level expenditure file,
unless the characteristic of interest is event specific.
In order to produce national estimates related to inpatient hospital utilization, expenditure and
sources of payment, the value in each record contributing to the estimates must be multiplied by
the weight (WTDPER96) contained on that record.
Example 1:
For example, the total number of hospital inpatient stays, regardless of the length of the hospital
stay for the civilian non-institutionalized population of the U.S. in 1996, is estimated as the sum
of the weight (WTDPER96) across all records. That is,
Sum of Wj= 26,526,275 (1)
Various estimates can be produced based on specific variables and subsets of records.
Example 2:
For example, the estimate for the mean out-of-pocket payment at the hospital inpatient stay level,
for hospital inpatient stays with expenditures should be calculated as the weighted mean of the
facility bill and doctor's bill paid by self/family. That is,
X bar =(Sum of WjXj) / (Sum of Wj)= $163.46 (2)
where Xj = IPFSF96Xj + IPDSF96Xj and Sum of Wj=25,915,821
for all records with IPEXP96Xj > 0 .
This gives $163.46 as the estimated mean amount of out-of-pocket payment of expenditures
associated with hospital inpatient stays (discharges) and 25,915,821 as an estimate of the total
number of hospital inpatient stays with expenditure. Both of these estimates are for the civilian
non-institutionalized population of the U.S. in 1996.
Example 3:
Another example would be to estimate the mean proportion of total expenditures paid by private
insurance for hospital inpatient stays with expenditures. This should be calculated as the weighted
mean of proportion of total expenditures paid by private insurance at the stay level. That is,
Y bar =(Sum of WjYj) / (Sum of Wj)=0.4133 where Sum of Wj=25,915,821 (3)
where Yi=(IPFPV96Xi + IPDPV96Xi) / IPEX96Xi for all records with IPEXP96Xj > 0.
This gives 0.4113 as the estimated mean proportion of total expenditures paid by private
insurance for hospital inpatient stays (discharges) with expenditures for the civilian non-institutionalized population of the U.S. in 1996.
Return To Table Of Contents
4.3 Estimates of the Number of Persons with Hospital Inpatient Stays
When calculating an estimate of the total number of persons with hospital inpatient stays, users
can use a person-level file (MEPS HC-011: Person Level Expenditures and Utilization) or the
Hospital Inpatient Stays file. The Hospital Inpatient Stays file must be used, when the measure of
interest is defined at the event level. For example, to estimate the number of persons, in the
civilian non-institutionalized population of the U.S., discharged from a hospital in1996 with at
least one hospital stay of 10 or more nights, this file must be used. This would be estimated as,
Sum of Wixi across all unique persons i on this file, (4)
where
Wi is the sampling weight(WTDPER96) for person i
and
Xi = 1 if NUMNIGHX GE 10 for any stay of person i
= 0 otherwise.
Prior to estimation users will need to take into consideration the 32 records with a missing value
for NUMNIGHX.
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4.4 Person-Based Ratio Estimates
4.4.1 Person-Based Ratio Estimates Relative to Persons with Hospital Inpatient
Use
This file may be used to derive person-based ratio estimates. However, when calculating ratio
estimates where the denominator is persons, care should be taken to properly define the unit of
analysis as person level. For example, the mean expense for persons with hospital inpatient stays
is estimated as,
(Sum of WiZi) / (Sum of Wi) across all unique persons i on this file, (5)
where
Wi is the sampling weight(WTDPER96) for person i
and
Zi = Sum of IPEXP96Xj across all stays for person
i.
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4.4.2 Person-Based Ratio Estimates Relative to the Entire Population
If the ratio relates to the entire population, this file cannot be used to calculate the denominator, as
only those persons with at least one hospital inpatient stay are represented on this data file. In this
case MEPS HC-011, which has data for all sampled persons, must be used to estimate the total
number of persons (i.e. those with use and those without use). For example, the proportion of
civilian non-institutionalized population of the U.S. with at least one hospital inpatient stay of
four or more days would be estimated as:
(Sum of WiZi) / (Sum of Wi) across all unique persons i on the person level file, (6)
where
Wi is the sampling weight(WTDPER96) for person i
and
Zi = 1 if NUMNIGHX j GE 4 for any stay of person i on the inpatient stay-level
file
= 0 otherwise for all remaining persons on the MEPS HC-011 file.
Prior to estimation users will need to take into consideration the 32 records with a missing value
for NUMNIGHX.
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4.5 Sampling Weights for Merging Previous Releases of MEPS Household Data
with the Hospital Inpatient Stays Data File
There have been several previous releases of MEPS Household Survey public use data. Unless a
variable name common to several tapes is provided, the sampling weights contained on these data
files are file-specific. The file-specific weights reflect minor adjustments to eligibility and
response indicators due to birth, death, or institutionalization among respondents.
In general, for estimates from a MEPS data file that do not require merging with variables from
other MEPS data files, the sampling weight(s) provided on that data file are the appropriate
weight(s). When merging a MEPS Household data file to another, the major analytical variable
(i.e. the dependent variable) determines the correct sampling weight to use.
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4.6 Variance Estimation
To obtain estimates of variability (such as the standard error of sample estimates or corresponding
confidence intervals) for estimates based on MEPS survey data, one needs to take into account the
complex sample design of MEPS. Various approaches can be used to develop such estimates of
variance including use of the Taylor series or various replication methodologies. Replicate weights
have not been developed for the MEPS 1996 data. Variables needed to implement a Taylor series
estimation approach are described in the paragraph below.
Using a Taylor Series approach, variance estimation strata and the variance estimation PSUs within
these strata must be specified. The corresponding variables on the MEPS full year utilization
database are VARSTR96 and VARPSU96, respectively. Specifying a "with replacement" design in
a computer software package such as SUDAAN (Shah, 1996) should provide standard errors
appropriate for assessing the variability of MEPS survey estimates. It should be noted that the
number of degrees of freedom associated with estimates of variability indicated by such a package
may not appropriately reflect the actual number available. For MEPS sample estimates for
characteristics generally distributed throughout the country (and thus the sample PSUs), there are over
100 degrees of freedom associated with the corresponding estimates of variance. The following
illustrates these concepts using two examples from Section 4.2.
Example 2 from Section 4.2
Using a Taylor Series approach, specifying VARSTR96 and VARPSU96 as the variance estimation
strata and PSUs (within these strata) respectively and specifying a "with replacement" design in the
computer software package SUDAAN will yield an estimate of standard error of $18.96 for the
estimated mean of out-of-pocket payment.
Example 3 from section 4.2
Using a Taylor Series approach, specifying VARSTR96 and VARPSU96 as the variance estimation
strata and PSUs (within these strata) respectively and specifying a "with replacement" design in the
computer software package SUDAAN will yield an estimate of standard error of 0.0159 for the
weighted mean proportion of total expenditures paid by private insurance.
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5.0 Merging/Linking MEPS Data Files
Data from this file can be used alone or in conjunction with other files. This section provides
instructions for linking the hospital stays files with other MEPS public use files, including: the
conditions file, the prescribed medicines file, and a person-level file.
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5.1 Linking a Person-Level File to the Hospital Inpatient Stays File
Merging characteristics of interest from person-level files (e.g., HC-008: 1996 Population
Characteristics and Utilization Data, or HC-011: 1996 Use and Expenditure File) expands the scope
of potential estimates. For example, to estimate the total number of hospital inpatient stays for persons
with specific characteristics (e.g., age, race, and sex), population characteristics from a person-level
file need to be merged onto the hospital inpatient stays file. This procedure is illustrated below. The
Appendix File (HC-010I) provides additional detail on how to merge MEPS data files.
1. Create data set PERS by sorting the the person-level file, HC003, by the person
identifier, DUPERSID. Keep only variables to be merged on to the hospital inpatient
stays file and DUPERSID.
2. Create data set STAZ by sorting the hospital inpatient stays file by person identifier,
DUPERSID.
3. Create final data set NEWSTAZ by merging these two files by DUPERSID, keeping
only records on the hospital inpatient stays file.
The following is an example of SAS code which completes these steps:
PROC SORT DATA=HC003(KEEP=DUPERSID AGE SEX EDUC)
OUT=PERSX;
BY DUPERSID;
RUN;
PROC SORT DATA=STAYS;
BY DUPERSID;
RUN;
DATA NEWSTAYS;
MERGE STAYS (IN=A) PERSX(IN=B);
BY DUPERSID;
IF A;
RUN;
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5.2 Linking the Hospital Inpatient Stays (HC-010D) to the Medical Conditions File
(HC-006) and/or the Prescribed Medicines File (HC-010A)
Due to survey design issues, there are limitations/caveats that an analyst must keep in mind when
linking the different files. Those limitations/caveats are listed below. For detailed linking examples,
including SAS code, analysts should refer to the Appendix File.
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5.3 Limitations/Caveats of RXLK (the Prescribed Medicine Link File)
The RXLK file provides a link from the MEPS event files to the prescribed medicine records on HC-010A. When using RXLK, analysts should keep in mind that one hospital inpatient stay can link to
more than one prescribed medicine record. Conversely, a prescribed medicine event may link to more
than one hospital inpatient stay or different types of events. When this occurs, it is up to the analyst
to determine how the prescribed medicine expenditures should be allocated among those medical
events.
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5.4 Limitations/Caveats of CLNK (the Medical Conditions Link File)
The CLNK provides a link from MEPS event files to the Medical Conditions File (HC-006). When
using the CLNK, analysts should keep in mind that (1) conditions are self-reported and (2) there may
be multiple conditions associated with a hospital inpatient stay. Users should also note that not all
hospital inpatient stays link to the condition file.
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6.0 Programming Information
The following are the technical specifications for the HC-010D data files, which are provided in
ASCII and SAS formats.
ASCII versions:
File Name: HC10CF1.DAT
Number of Observations: 2,207
Number of Variables: 86
Record Length: 389
Record Format: fixed
Record Identifier and Sort Key: EVNTIDX
File Name: HC10CF2.DAT
Number of Observations: 2,207
Number of Variables: 30
Record Length: 220
Record Format: fixed
Record Identifier and Sort Key: EVNTIDX
SAS Transport versions:
File Name: HC10CF1.SSP
SAS Name: HC10CF1
Number of Observations: 2,207
Number of Variables: 86
Record Identifier and Sort Key: EVNTIDX
File Name: HC10CF2.SSP
SAS Name: HC10CF2
Number of Observations: 2,207
Number of Variables: 30
Record Identifier and Sort Key: EVNTIDX
Return To Table Of Contents
References
Cohen, S.B. (1998). Sample Design of the 1996 Medical Expenditure Panel Survey Medical Provider
Component. Journal of Economic and Social Measurement. Vol 24, 25-53.
Cohen, S.B. (1997). Sample Design of the 1996 Medical Expenditure Panel Survey Household
Component. Rockville (MD): Agency for Health Care Policy and Research; 1997. MEPS
Methodology Report, No. 2. AHCPR Pub. No. 97-0027.
Cohen, J.W. (1997). Design and Methods of the Medical Expenditure Panel Survey Household
Component. Rockville (MD): Agency for Health Care Policy and Research; 1997. MEPS
Methodology Report, No. 1. AHCPR Pub. No. 97-0026.
Cohen, S.B. (1996). The Redesign of the Medical Expenditure Panel Survey: A Component of the
DHHS Survey Integration Plan. Proceedings of the COPAFS Seminar on Statistical Methodology
in the Public Service.
Cox, B.G. and Cohen, S.B. (1985). Chapter 8: Imputation Procedures to Compensate for Missing
Responses to Data Items. In Methodological Issues for Health Care Surveys. Marcel Dekker, New
York.
Health Care Financing Administration (1980). International Classification of Diseases, 9th Revision,
Clinical Modification (ICD-CM). Vol. 1. (DHHS Pub. No. (PHS) 80-1260). DHHS: U.S. Public
Health Services.
Monheit, A.C., Wilson, R., and Arnett, III, R.H. (Editors). Informing American Health Care Policy.
(1999). Jossey-Bass Inc, San Francisco.
Shah, B.V., Barnwell, B.G., Bieler, G.S., Boyle, K.E., Folsom, R.E., Lavange, L., Wheeless, S.C.,
and Williams, R. (1996). Technical Manual: Statistical Methods and Algorithms Used in SUDAAN
Release 7.0, Research Triangle Park, NC: Research Triangle Institute.
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Attachment 1
Definitions
Dwelling Units, Reporting Units, Families, and Persons The definitions of Dwelling Units (DUs)
and Group Quarters in the MEPS Household Survey are generally consistent with the definitions
employed for the National Health Interview Survey. The dwelling unit ID (DUID) is a five-digit
random ID number assigned after the case was sampled for MEPS. The person number (PID)
uniquely identifies all persons within the dwelling unit. The variable DUPERSID is the combination
of the variables DUID and PID.
A Reporting Unit (RU) is a person or group of persons in the sampled dwelling unit who are related
by blood, marriage, adoption or other family association, and who are to be interviewed as a group
in MEPS. Thus, the RU serves chiefly as a family-based "survey operations" unit rather than an
analytic unit. Regardless of the legal status of their association, two persons living together as a
"family" unit were treated as a single reporting unit if they chose to be so identified.
Unmarried college students under 24 years of age who usually live in the sampled household, but
were living away from home and going to school at the time of the Round 1 MEPS interview, were
treated as a Reporting Unit separate from that of their parents for the purpose of data collection.
These variables can be found on MEPS person level files.
In-Scope A person was classified as in-scope (INSCOPE) if he or she was a member of the U.S.
civilian, non-institutionalized population at some time during the Round 1 interview. This variable
can be found on MEPS person level files.
Keyness The term "keyness" is related to an individual's chance of being included in MEPS. A
person is key if that person is appropriately linked to the set of 1995 NHIS sampled households
designated for inclusion in MEPS. Specifically, a key person either was a member of an NHIS
household at the time of the NHIS interview, or became a member of such a household after being
out-of-scope prior to joining that household (examples of the latter situation include newborns and
persons returning from military service, an institution, or living outside the United States).
A non-key person is one whose chance of selection for the NHIS (and MEPS) was associated with
a household eligible but not sampled for the NHIS, who happened to have become a member of a
MEPS reporting unit by the time of the MEPS Round 1 interview. MEPS data, (e.g., utilization and
income) were collected for the period of time a non-key person was part of the sampled unit to permit
family level analyses. However, non-key persons who leave a sample household would not be
recontacted for subsequent interviews. Non-key individuals are not part of the target sample used to
obtain person level national estimates.
It should be pointed out that a person may be key even though not part of the civilian, non-institutionalized portion of the U.S population. For example, a person in the military may be living
with his or her civilian spouse and children in a household sampled for the 1995 NHIS. The person
in the military would be considered a key person for MEPS. However, such a person would not
receive a person-level sample weight so long as he or she was in the military. All key persons who
participated in the first round of the 1996 MEPS received a person level sample weight except those
who were in the military. The variable indicating "keyness" is KEYNESS. This variable can be
found on MEPS person level files.
Eligibility The eligibility of a person for MEPS pertains to whether or not data were to be collected
for that person. All key, in-scope persons of a sampled RU were eligible for data collection. The only
non-key persons eligible for data collection were those who happened to be living in the same RU as
one or more key persons, and their eligibility continued only for the time that they were living with
a key person. The only out-of-scope persons eligible for data collection were those who were living
with key in-scope persons, again only for the time they were living with a key person. Only military
persons meet this description. A person was considered eligible if they were eligible at any time
during Round 1. The variable indicating "eligibility" is ELIGRND1, where 1 is coded for persons
eligible for data collection for at least a portion of the Round 1 reference period, and 2 is coded for
persons not eligible for data collection at any time during the first round reference period. This
variable can be found on MEPS person level files.
Pre-imputed - This means that only a series of logical edits were applied to the HC data to correct
for several problems including outliers, copayments or charges reported as total payments, and
reimbursed amounts counted as out of pocket payments. Missing data remains.
Unimputed - This means that only a series of logical edits were applied to the MPC data to correct
for several problems including outliers, copayments or charges reported as total payments, and
reimbursed amounts counted as out of pocket payments. This data was used as the imputation source
to account for missing HC data.
Imputation -Imputation is more often used for item missing data adjustment through the use of
predictive models for the missing data, based on data available on the same (or similar) cases. Hot-deck imputation creates a data set with complete data for all nonrespondent cases, often by
substituting the data from a respondent case that resembles the nonrespondent on certain known
variables.
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D. Codebooks
(link to separate file)
Return To Table Of Contents
E. Variable-Source Crosswalk
MEPS HC010D: 1996 HOSPITAL INPATIENT STAYS
File 1:
Survey Administration and ID Variables
Variable |
Description |
Source |
DUID |
Dwelling unit ID |
Assigned in sampling |
PID |
Person number |
Assigned in sampling |
DUPERSID |
Sample person ID (DUID + PID) |
Assigned in sampling |
EVENTIDX |
Event ID : DUPERSID + EVENTN |
Assigned in Sampling |
EVENTRN |
Round number |
CAPI derived |
ERHEVIDX |
Emergency Room/Hospital Stay Link |
Constructed |
FFID11X |
Flat fee ID |
CAPI derived |
MPCDATA |
MPC Data Indicator |
Constructed |
MBLINK |
Mother/Baby expenditure Flag |
Constructed |
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Characteristics of Hospital Inpatient Stay
Variable |
Description |
Source |
IPBEGYR |
Event start date – year |
CAPI derived |
IPBEGMM |
Event start date – month |
CAPI derived |
IPBEGDD |
Event start date – day |
CAPI derived |
IPENDYR |
Event end date – year |
CAPI derived |
IPENDMM |
Event end date – month |
CAPI derived |
IPENDDD |
Event end date – day |
CAPI derived |
NUMNIGHX |
Number of nights stayed at Hospital
- Edited |
(Edited/imputed) |
NUMNIGHT |
Number of nights stayed at Hospital |
HS01 |
EMERROOM |
Did stay begin with emergency room
visit |
HS02 |
SPECCOND |
Hospital stay related to condition |
HS03 |
RSNINHOS |
Reason entered hospital |
HS05 |
ANYOPER |
Any operations or surgery performed |
HS06 |
SURGPROC |
Main surgical procedure |
HS07 |
VAPLACE |
Hospital is a VA facility |
Constructed |
IPICD1X |
3 digit ICD-9 condition code |
HS02 (Edited) |
IPICD2X |
3 digit ICD-9 condition code |
HS02 (Edited) |
IPICD3X |
3 digit ICD-9 condition code |
HS02 (Edited) |
IPICD4X |
3 digit ICD-9 condition code |
HS02 (Edited) |
IPPRO1X |
2 digit ICD-9 procedure code |
HS02 (Edited) |
IPPRO2X |
2 digit ICD-9 procedure code |
HS02 (Edited) |
IPCCC1X |
Modified Clinical Classification
Code |
Constructed/Edited |
IPCCC2X |
Modified Clinical Classification
Code |
Constructed/Edited |
IPCCC3X |
Modified Clinical Classification
Code |
Constructed/Edited |
IPCCC4X |
Modified Clinical Classification
Code |
Constructed/Edited |
NUMCOND |
Total number of COND records linked
to this event |
Constructed |
DSCHPMED |
Medicines prescribed at discharge |
HS08 |
DROUTSID |
Any of the DRS seen outside the
hospital |
HS10 |
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Flat Fee Variables
| Variable |
Description |
Source |
FFIPTYPX |
Stem or Leaf of Flat Fee Group |
FF01, FF02 |
FFIP96 |
# Hospital stays in flat fee in
1996 |
FF02 |
FFTOT96 |
Total # visits (pure/mixed) in flat
fee for 1996 |
FF02 (edited)? |
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Imputed Total Expenditure Variables
| Variable |
Description |
Source |
IPEXP96X |
Total expenditure for hospital inpatient stay |
Constructed |
IPTCH96X |
Total charge for hospital inpatient stay |
Constructed |
Imputed Facility Expenditure Variables
| Variable |
Description |
Source |
IPFSF96X |
Facility amount paid, family (imputed) |
Imputed |
IPFMR96X |
Facility amount paid, Medicare (imputed) |
Imputed |
IPFMD96X |
Facility amount paid, Medicaid (imputed) |
Imputed |
IPFPV96X |
Facility amount paid, private insurance (imputed) |
Imputed |
IPFVA96X |
Facility amount paid, Veterans (imputed) |
Imputed |
IPFCH96X |
Facility amount paid, CHAMP/CHAMPVA (imputed) |
Imputed |
IPFOF96X |
Facility amount paid, other federal (imputed) |
Imputed |
IPFSL96X |
Facility amount paid, state/local govt. (imputed) |
Imputed |
IPFWC96X |
Facility amount paid, Worker’s Comp (imputed) |
Imputed |
IPFOR96X |
Facility amount paid, other private (imputed) |
Imputed |
IPFOU96X |
Facility amount paid, other public (imputed) |
Imputed |
IPFOT96X |
Facility amount paid, other insurance (imputed) |
Imputed |
IPFXP96X |
Facility sum of payments IPFSF96X – IPFOT96X |
Constructed |
IPFTC96X |
Facility total charge (imputed) |
Imputed |
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Imputed Separately Billing Physician Expenditure Variables
| Variable |
Description |
Source |
IPDSF96X |
Doctor amount paid, family (imputed) |
Imputed |
IPDMR96X |
Doctor amount paid, Medicare (imputed) |
Imputed |
IPDMD96X |
Doctor amount paid, Medicaid (imputed) |
Imputed |
IPDPV96X |
Doctor amount paid, private insurance (imputed) |
Imputed |
IPDVA96X |
Doctor amount paid, Veterans (imputed) |
Imputed |
IPDCH96X |
Doctor amount paid, CHAMP/CHAMPVA (imputed) |
Imputed |
IPDOF96X |
Doctor amount paid, other federal (imputed) |
Imputed |
IPDSL96X |
Doctor amount paid, state/local govt. (imputed) |
Imputed |
IPDWC96X |
Doctor amount paid, Worker’s Comp (imputed) |
Imputed |
IPDOR96X |
Doctor amount paid, other private (imputed) |
Imputed |
IPDOU96X |
Doctor amount paid, other public (imputed) |
Imputed |
IPDOT96X |
Doctor amount paid, other insurance (imputed) |
Imputed |
IPDXP96X |
Doctor sum of payments IPPSF96X – IPPOT96X |
Constructed |
IPDTC96X |
Doctor total charge (imputed) |
Imputed |
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Imputation Flag Variables
| Variable |
Description |
Source |
IMPIPFSF |
Imputation flag for IPFSF96X |
Constructed |
IMPIPFMR |
Imputation flag for IPFMR96X |
Constructed |
IMPIPFMD |
Imputation flag for IPFMD96X |
Constructed |
IMPIPFPV |
Imputation flag for IPFPV96X |
Constructed |
IMPIPFVA |
Imputation flag for IPFVA96X |
Constructed |
IMPIPFCH |
Imputation flag for IPFCH96X |
Constructed |
IMPIPFOF |
Imputation flag for IPFOF96X |
Constructed |
IMPIPFSL |
Imputation flag for IPFSL96X |
Constructed |
IMPIPFWC |
Imputation flag for IPFWC96X |
Constructed |
IMPIPFOR |
Imputation flag for IPFOR96X |
Constructed |
IMPIPFOU |
Imputation flag for IPFOU96X |
Constructed |
IMPIPFOT |
Imputation flag for IPFOT96X |
Constructed |
IMPIPCHG |
Imputation flag for IPFTC96X |
Constructed |
IMPIPNUM |
Number of separately billing physicians associated
with hospital stay |
Constructed |
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Weights
| Variable |
Description |
Source |
WTDPER96 |
Poverty/Mortality Adjusted Person weight,
1996 |
Constructed |
VARSTR96 |
Variance estimation stratum, 1996 |
Constructed |
VARPSU96 |
Variance estimation PSU, 1996 |
Constructed |
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File 2:Pre-imputed/Unimputed Expenditure Variables
| Variable |
Description |
Source |
DUID |
Dwelling unit ID |
Assigned in sampling |
PID |
Person number |
Assigned in sampling |
DUPERSID |
Sample person ID (DUID + PID) |
Assigned in sampling |
EVENTIDX |
Event ID : DUPERSID + EVENTN |
Assigned in Sampling |
HHSFFIDX |
Household reported flat fee identifier (unedited) |
CAPI generated |
IPSF96H |
Household reported amount paid, family
(pre-imputed) |
CP07, CP09, CP11-CP340V2 |
IPMR96H |
Household reported amount paid, Medicare
(pre-imputed) |
CP07, CP09, CP11-CP34 OV2 |
IPMD96H |
Household reported amount paid, Medicaid
(pre-imputed) |
CP07, CP09, CP11-CP34 OV2 |
IPPV96H |
Household reported amount paid, private insurance
(pre-imputed) |
CP07, CP09, CP11-CP34 OV2 |
IPVA96H |
Household reported amount paid, Veterans
(pre-imputed) |
CP07, CP09, CP11-CP34 OV2 |
IPCH96H |
Household reported amount paid, CHAMP/CHAMPVA
(pre-imputed) |
CP07, CP09, CP11-CP34 OV2 |
IPOF96H |
Household reported amount paid, other federal
(pre-imputed) |
CP07, CP09, CP11-CP34 OV2 |
IPSL96H |
Household reported amount paid, state/local govt.
(pre-imputed) |
CP07, CP09, CP11-CP34 OV2 |
IPWC96H |
Household reported amount paid, Worker’s Comp
(pre-imputed) |
CP07, CP09, CP11-CP34 OV2 |
IPOT96H |
Household reported amount paid, other insurance.
(pre-imputed) |
CP07, CP09, CP11-CP34OV2 |
IPTC96H |
Household reported total charge (pre-imputed) |
CP09A, CP09OV |
IPSF96M |
MPC reported amount paid, family (unimputed) |
8, 9(a.), 12(a-d), 18, 19(a.), 22(a-d) |
IPMR96M |
MPC reported amount paid, Medicare (unimputed) |
8, 9(b.), 12(a-d), 18, 19(b.), 22(a-d) |
IPMD96M |
MPC reported amount paid, Medicaid (unimputed) |
8, 9(c.), 12(a-d), 18, 19(c.), 22(a-d) |
IPPV96M |
MPC reported amount paid, private insurance (unimputed) |
8, 9(d.), 12(a-d), 18, 19(d.), 22(a-d) |
IPVA96M |
MPC reported amount paid, Veterans (unimputed) |
8, 9(e.), 12(a-d), 18, 19(e.), 22(a-d) |
IPCH96M |
MPC reported amount paid, CHAMP/CHAMPVA (unimputed) |
8, 9(f.), 12(a-d), 18, 19(f.), 22(a-d) |
IPOF96M |
MPC reported amount paid, other federal (unimputed) |
8, 9(g.), 12(a-d), 18, 19(g.), 22(a-d) |
IPSL96M |
MPC reported amount paid, state/local govt. (unimputed) |
8, 9(g.), 12(a-d), 18, 19(g.), 22(a-d) |
IPWC96M |
MPC reported amount paid, Worker’s Comp (unimputed) |
8, 9(g.), 12(a-d), 18, 19(g.), 22(a-d) |
IPOT96M |
MPC reported amount paid, other insurance (unimputed) |
8, 9(g.), 12(a-d), 18, 19(g.), 22(a-d) |
IPTC96M |
MPC reported total charge (unimputed) |
7, 17(a, b), 18 |
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Weights
| Variable |
Description |
Source |
WTDPER96 |
Poverty/Mortality Adjusted Person weight,
1996 |
Constructed |
VARSTR96 |
Variance estimation stratum, 1996 |
Constructed |
VARPSU96 |
Variance estimation PSU, 1996 |
Constructed |
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